{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import wfdb as wf\n",
    "import numpy as np\n",
    "from datasets import mitdb as dm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 48 record files\n"
     ]
    }
   ],
   "source": [
    "# Load paths of avaliable data files\n",
    "\n",
    "records = dm.get_records()\n",
    "print('There are {} record files'.format(len(records)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading file: data/mitdb\\100\n"
     ]
    }
   ],
   "source": [
    "# Select one of them\n",
    "path = records[0]\n",
    "print('Loading file:', path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data/mitdb\\\\100'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "record = wf.rdrecord(path)\n",
    "wf.plot_wfdb(record=record, title='Record 100 from MIT-BIH Arrhythmia Database')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fs': 360,\n",
       " 'sig_len': 650000,\n",
       " 'n_sig': 2,\n",
       " 'base_date': None,\n",
       " 'base_time': None,\n",
       " 'units': ['mV', 'mV'],\n",
       " 'sig_name': ['MLII', 'V5'],\n",
       " 'comments': ['69 M 1085 1629 x1', 'Aldomet, Inderal']}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signals, fields = wf.rdsamp(path)\n",
    "fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.145, -0.065],\n",
       "       [-0.145, -0.065],\n",
       "       [-0.145, -0.065],\n",
       "       ...,\n",
       "       [-0.675, -0.365],\n",
       "       [-0.765, -0.335],\n",
       "       [-1.28 ,  0.   ]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'fs': 360,\n",
       " 'sig_len': 650000,\n",
       " 'n_sig': 2,\n",
       " 'base_date': None,\n",
       " 'base_time': None,\n",
       " 'units': ['mV', 'mV'],\n",
       " 'sig_name': ['MLII', 'V5'],\n",
       " 'comments': ['69 M 1085 1629 x1', 'Aldomet, Inderal']}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "\n",
    "display(signals)\n",
    "display(fields)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annotation = wf.rdann(path, 'atr')\n",
    "annotation.fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wf.plot_wfdb(annotation=annotation, time_units='minutes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read a wfdb record and annotation. Plot all channels, and the annotation on top of channel 0\n",
    "\n",
    "record = wf.rdrecord(path, sampto=15000)\n",
    "annotation = wf.rdann(path, 'atr', sampto=15000)\n",
    "\n",
    "wf.plot_wfdb(record=record, annotation=annotation,\n",
    "               title='Record 100 from MIT-BIH Arrhythmia Database',\n",
    "               time_units='seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MLII', 'V5']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record.sig_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ECG channel type: MLII\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Select one of the channels (there are two)\n",
    "chid = 0\n",
    "data = record.p_signal\n",
    "channel = data[:, chid]\n",
    "\n",
    "print ('ECG channel type:', record.sig_name[chid])\n",
    "\n",
    "# Plot only the first 2000 samples\n",
    "howmany = 2000\n",
    "\n",
    "# Calculate time values in seconds\n",
    "times = np.arange(howmany, dtype = 'float') / record.fs\n",
    "plt.plot(times, channel[ : howmany])\n",
    "plt.xlabel('Time [s]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(record)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = 'data/mitdb\\\\100'\n",
    "\n",
    "record = wf.rdrecord(path, sampto=15000)\n",
    "annotation = wf.rdann(path, 'atr', sampto=15000)\n",
    "\n",
    "wf.plot_wfdb(record=record, annotation=annotation,\n",
    "               title='Record 100 from MIT-BIH Arrhythmia Database',\n",
    "               time_units='seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ECG channel type: MLII\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Select one of the channels (there are two)\n",
    "chid = 0\n",
    "data = record.p_signal\n",
    "channel = data[:, chid]\n",
    "\n",
    "print ('ECG channel type:', record.sig_name[chid])\n",
    "\n",
    "# Plot only the first 2000 samples\n",
    "howmany = 2000\n",
    "\n",
    "# Calculate time values in seconds\n",
    "times = np.arange(howmany, dtype = 'float') / record.fs\n",
    "plt.plot(times, channel[ : howmany])\n",
    "plt.xlabel('Time [s]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on Record in module wfdb.io.record object:\n",
      "\n",
      "class Record(BaseRecord, wfdb.io._header.HeaderMixin, wfdb.io._signal.SignalMixin)\n",
      " |  The class representing single segment WFDB records.\n",
      " |  \n",
      " |  Record objects can be created using the initializer, by reading a WFDB\n",
      " |  header with `rdheader`, or a WFDB record (header and associated dat files)\n",
      " |  with `rdrecord`.\n",
      " |  \n",
      " |  The attributes of the Record object give information about the record as\n",
      " |  specified by: https://www.physionet.org/physiotools/wag/header-5.htm\n",
      " |  \n",
      " |  In addition, the d_signal and p_signal attributes store the digital and\n",
      " |  physical signals of WFDB records with at least one channel.\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> record = wfdb.Record(record_name='r1', fs=250, n_sig=2, sig_len=1000,\n",
      " |                       file_name=['r1.dat','r1.dat'])\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      Record\n",
      " |      BaseRecord\n",
      " |      wfdb.io._header.HeaderMixin\n",
      " |      wfdb.io._header.BaseHeaderMixin\n",
      " |      wfdb.io._signal.SignalMixin\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __eq__(self, other, verbose=False)\n",
      " |      Return self==value.\n",
      " |  \n",
      " |  __init__(self, p_signal=None, d_signal=None, e_p_signal=None, e_d_signal=None, record_name=None, n_sig=None, fs=None, counter_freq=None, base_counter=None, sig_len=None, base_time=None, base_date=None, file_name=None, fmt=None, samps_per_frame=None, skew=None, byte_offset=None, adc_gain=None, baseline=None, units=None, adc_res=None, adc_zero=None, init_value=None, checksum=None, block_size=None, sig_name=None, comments=None)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  wrsamp(self, expanded=False, write_dir='')\n",
      " |      Write a wfdb header file and any associated dat files from this\n",
      " |      object.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      expanded : bool, optional\n",
      " |          Whether to write the expanded signal (e_d_signal) instead\n",
      " |          of the uniform signal (d_signal).\n",
      " |      write_dir : str, optional\n",
      " |          The directory in which to write the files.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __hash__ = None\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from BaseRecord:\n",
      " |  \n",
      " |  check_field(self, field, required_channels='all')\n",
      " |      Check whether a single field is valid in its basic form. Does\n",
      " |      not check compatibility with other fields.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      field : str\n",
      " |          The field name\n",
      " |      required_channels : list, optional\n",
      " |          Used for signal specification fields. All channels are\n",
      " |          checked for their integrity if present, but channels that do\n",
      " |          not lie in this field may be None.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      This function is called from wrheader to check fields before\n",
      " |      writing. It is also supposed to be usable at any point to\n",
      " |      check a specific field.\n",
      " |  \n",
      " |  check_read_inputs(self, sampfrom, sampto, channels, physical, smooth_frames, return_res)\n",
      " |      Ensure that input read parameters (from rdsamp) are valid for\n",
      " |      the record\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from BaseRecord:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from wfdb.io._header.HeaderMixin:\n",
      " |  \n",
      " |  check_field_cohesion(self, rec_write_fields, sig_write_fields)\n",
      " |      Check the cohesion of fields used to write the header\n",
      " |  \n",
      " |  get_write_fields(self)\n",
      " |      Get the list of fields used to write the header, separating\n",
      " |      record and signal specification fields. Returns the default\n",
      " |      required fields, the user defined fields,\n",
      " |      and their dependencies.\n",
      " |      \n",
      " |      Does NOT include `d_signal` or `e_d_signal`.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      rec_write_fields : list\n",
      " |          Record specification fields to be written. Includes\n",
      " |          'comment' if present.\n",
      " |      sig_write_fields : dict\n",
      " |          Dictionary of signal specification fields to be written,\n",
      " |          with values equal to the channels that need to be present\n",
      " |          for each field.\n",
      " |  \n",
      " |  set_default(self, field)\n",
      " |      Set the object's attribute to its default value if it is missing\n",
      " |      and there is a default.\n",
      " |      \n",
      " |      Not responsible for initializing the\n",
      " |      attribute. That is done by the constructor.\n",
      " |  \n",
      " |  set_defaults(self)\n",
      " |      Set defaults for fields needed to write the header if they have\n",
      " |      defaults.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      - This is NOT called by `rdheader`. It is only automatically\n",
      " |        called by the gateway `wrsamp` for convenience.\n",
      " |      - This is also not called by `wrheader` since it is supposed to\n",
      " |        be an explicit function.\n",
      " |      - This is not responsible for initializing the attributes. That\n",
      " |        is done by the constructor.\n",
      " |      \n",
      " |      See also `set_p_features` and `set_d_features`.\n",
      " |  \n",
      " |  wr_header_file(self, rec_write_fields, sig_write_fields, write_dir)\n",
      " |      Write a header file using the specified fields. Converts Record\n",
      " |      attributes into appropriate wfdb format strings.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      \n",
      " |      rec_write_fields : list\n",
      " |          List of record specification fields to write\n",
      " |      sig_write_fields : dict\n",
      " |          Dictionary of signal specification fields to write, values\n",
      " |          being equal to a list of channels to write for each field.\n",
      " |      write_dir : str\n",
      " |          The directory in which to write the header file\n",
      " |  \n",
      " |  wrheader(self, write_dir='')\n",
      " |      Write a wfdb header file. The signals are not used. Before\n",
      " |      writing:\n",
      " |      - Get the fields used to write the header for this instance.\n",
      " |      - Check each required field.\n",
      " |      - Check that the fields are cohesive with one another.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      write_dir : str, optional\n",
      " |          The output directory in which the header is written.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      This function does NOT call `set_defaults`. Essential fields\n",
      " |      must be set beforehand.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from wfdb.io._header.BaseHeaderMixin:\n",
      " |  \n",
      " |  get_write_subset(self, spec_type)\n",
      " |      Get a set of fields used to write the header; either 'record'\n",
      " |      or 'signal' specification fields. Helper function for\n",
      " |      `get_write_fields`. Gets the default required fields, the user\n",
      " |      defined fields, and their dependencies.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      spec_type : str\n",
      " |          The set of specification fields desired. Either 'record' or\n",
      " |          'signal'.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      write_fields : list or dict\n",
      " |          For record fields,  returns a list of all fields needed. For\n",
      " |          signal fields, it returns a dictionary of all fields needed,\n",
      " |          with keys = field and value = list of channels that must be\n",
      " |          present for the field.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from wfdb.io._signal.SignalMixin:\n",
      " |  \n",
      " |  adc(self, expanded=False, inplace=False)\n",
      " |      Performs analogue to digital conversion of the physical signal stored\n",
      " |      in p_signal if expanded is False, or e_p_signal if expanded is True.\n",
      " |      \n",
      " |      The p_signal/e_p_signal, fmt, gain, and baseline fields must all be\n",
      " |      valid.\n",
      " |      \n",
      " |      If inplace is True, the adc will be performed inplace on the variable,\n",
      " |      the d_signal/e_d_signal attribute will be set, and the\n",
      " |      p_signal/e_p_signal field will be set to None.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      expanded : bool, optional\n",
      " |          Whether to transform the `e_p_signal` attribute (True) or\n",
      " |          the `p_signal` attribute (False).\n",
      " |      inplace : bool, optional\n",
      " |          Whether to automatically set the object's corresponding\n",
      " |          digital signal attribute and set the physical\n",
      " |          signal attribute to None (True), or to return the converted\n",
      " |          signal as a separate variable without changing the original\n",
      " |          physical signal attribute (False).\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      d_signal : numpy array, optional\n",
      " |          The digital conversion of the signal. Either a 2d numpy\n",
      " |          array or a list of 1d numpy arrays.\n",
      " |      \n",
      " |      Examples:\n",
      " |      ---------\n",
      " |      >>> import wfdb\n",
      " |      >>> record = wfdb.rdsamp('sample-data/100')\n",
      " |      >>> d_signal = record.adc()\n",
      " |      >>> record.adc(inplace=True)\n",
      " |      >>> record.dac(inplace=True)\n",
      " |  \n",
      " |  calc_adc_params(self)\n",
      " |      Compute appropriate adc_gain and baseline parameters for adc\n",
      " |      conversion, given the physical signal and the fmts.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      adc_gains : list\n",
      " |          List of calculated `adc_gain` values for each channel.\n",
      " |      baselines : list\n",
      " |          List of calculated `baseline` values for each channel.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      This is the mapping equation:\n",
      " |          `digital - baseline / adc_gain = physical`\n",
      " |          `physical * adc_gain + baseline = digital`\n",
      " |      \n",
      " |      The original WFDB library stores `baseline` as int32.\n",
      " |      Constrain abs(adc_gain) <= 2**31 == 2147483648\n",
      " |      \n",
      " |      This function does carefully deal with overflow for calculated\n",
      " |      int32 `baseline` values, but does not consider over/underflow\n",
      " |      for calculated float `adc_gain` values.\n",
      " |  \n",
      " |  calc_checksum(self, expanded=False)\n",
      " |      Calculate the checksum(s) of the d_signal (expanded=False)\n",
      " |      or e_d_signal field (expanded=True)\n",
      " |  \n",
      " |  check_sig_cohesion(self, write_fields, expanded)\n",
      " |      Check the cohesion of the d_signal/e_d_signal field with the other\n",
      " |      fields used to write the record\n",
      " |  \n",
      " |  convert_dtype(self, physical, return_res, smooth_frames)\n",
      " |  \n",
      " |  dac(self, expanded=False, return_res=64, inplace=False)\n",
      " |      Performs the digital to analogue conversion of the signal stored\n",
      " |      in `d_signal` if expanded is False, or `e_d_signal` if expanded\n",
      " |      is True.\n",
      " |      \n",
      " |      The d_signal/e_d_signal, fmt, gain, and baseline fields must all be\n",
      " |      valid.\n",
      " |      \n",
      " |      If inplace is True, the dac will be performed inplace on the\n",
      " |      variable, the p_signal/e_p_signal attribute will be set, and the\n",
      " |      d_signal/e_d_signal field will be set to None.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      expanded : bool, optional\n",
      " |          Whether to transform the `e_d_signal attribute` (True) or\n",
      " |          the `d_signal` attribute (False).\n",
      " |      inplace : bool, optional\n",
      " |          Whether to automatically set the object's corresponding\n",
      " |          physical signal attribute and set the digital signal\n",
      " |          attribute to None (True), or to return the converted\n",
      " |          signal as a separate variable without changing the original\n",
      " |          digital signal attribute (False).\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      p_signal : numpy array, optional\n",
      " |          The physical conversion of the signal. Either a 2d numpy\n",
      " |          array or a list of 1d numpy arrays.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> import wfdb\n",
      " |      >>> record = wfdb.rdsamp('sample-data/100', physical=False)\n",
      " |      >>> p_signal = record.dac()\n",
      " |      >>> record.dac(inplace=True)\n",
      " |      >>> record.adc(inplace=True)\n",
      " |  \n",
      " |  set_d_features(self, do_adc=False, single_fmt=True, expanded=False)\n",
      " |      Use properties of the digital signal field to set the following\n",
      " |      features: n_sig, sig_len, init_value, checksum, and possibly\n",
      " |      *(fmt, adc_gain, baseline).\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      do_adc : bools\n",
      " |          Whether to use the physical signal field to perform adc\n",
      " |          conversion to get the digital signal field beforehand.\n",
      " |      single_fmt : bool\n",
      " |          Whether to use a single digital format during adc, if it is\n",
      " |          performed.\n",
      " |      expanded : bool\n",
      " |          Whether to use the `e_d_signal` or `d_signal` field.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      Regarding adc conversion:\n",
      " |      - If fmt is unset:\n",
      " |        - Neither adc_gain nor baseline may be set. If the digital values\n",
      " |          used to store the signal are known, then the file format should\n",
      " |          also be known.\n",
      " |        - The most appropriate fmt for the signals will be calculated and the\n",
      " |          `fmt` attribute will be set. Given that neither `adc_gain` nor\n",
      " |          `baseline` is allowed to be set, optimal values for those fields will\n",
      " |          then be calculated and set as well.\n",
      " |      - If fmt is set:\n",
      " |        - If both adc_gain and baseline are unset, optimal values for those\n",
      " |          fields will be calculated the fields will be set.\n",
      " |        - If both adc_gain and baseline are set, the function will continue.\n",
      " |        - If only one of adc_gain and baseline are set, this function will\n",
      " |          raise an error. It makes no sense to know only one of those fields.\n",
      " |      - ADC will occur after valid values for fmt, adc_gain, and baseline are\n",
      " |        present, using all three fields.\n",
      " |  \n",
      " |  set_p_features(self, do_dac=False, expanded=False)\n",
      " |      Use properties of the physical signal field to set the following\n",
      " |      features: n_sig, sig_len.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      do_dac : bool\n",
      " |          Whether to use the digital signal field to perform dac\n",
      " |          conversion to get the physical signal field beforehand.\n",
      " |      expanded : bool\n",
      " |          Whether to use the `e_p_signal` or `p_signal` field. If\n",
      " |          True, the `samps_per_frame` attribute is also required.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      Regarding dac conversion:\n",
      " |        - fmt, gain, and baseline must all be set in order to perform\n",
      " |          dac.\n",
      " |        - Unlike with adc, there is no way to infer these fields.\n",
      " |        - Using the fmt, gain and baseline fields, dac is performed,\n",
      " |          and (e_)p_signal is set.\n",
      " |      \n",
      " |      *Developer note: Seems this function will be very infrequently used.\n",
      " |       The set_d_features function seems far more useful.\n",
      " |  \n",
      " |  smooth_frames(self, sigtype='physical')\n",
      " |      Convert expanded signals with different samples/frame into\n",
      " |      a uniform numpy array.\n",
      " |      \n",
      " |      Input parameters\n",
      " |      - sigtype (default='physical'): Specifies whether to mooth\n",
      " |        the e_p_signal field ('physical'), or the e_d_signal\n",
      " |        field ('digital').\n",
      " |  \n",
      " |  wr_dat_files(self, expanded=False, write_dir='')\n",
      " |      Write each of the specified dat files\n",
      " |  \n",
      " |  wr_dats(self, expanded, write_dir)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(record)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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